The unpredictability of seizures has a profound impact on their safety of people with epilepsy. Accurate seizure forecasting would greatly improve an individuals' quality of life, potentially enabling pre-emptive administration of therapies or allowing steps to ensure personal safety to be undertaken.
It is well established that seizures in many patients are preceded by a measurable change in brain state. Attempts have been made in the past to implement methods for forecasting whether a seizure is going to occur based on these brain state changes. To date, such techniques for forecasting seizures suffer from poor generalizability due to the relatively short duration of available historical data.
The first human trial for an implantable warning system was reported in Cook et al, 2013 (Prediction of seizure likelihood with a long-term, implanted seizure advisory system in patients with drug-resistant epilepsy: a first-in-man study. The Lancet Neurology 2013; 12(6): 563-71), the contents of which is hereby incorporated by reference in its entirety. This study demonstrated the viability of seizure forecasting from intracranial electrical recordings (electroencephalography) for patients with intractable epilepsy. The resultant increase in available long-term data has inspired a renewed focus on implementing predictive methods that are both patient- and seizure-specific.
Despite recent forecasting advances, traditional assessment metrics for seizure prediction continue to be based on categorical statements—a seizure either will or will not happen—and are inappropriate for assessing probabilistic forecasts.
Any discussion of documents, acts, materials, devices, articles or the like which has been included in the present specification is not to be taken as an admission that any or all of these matters form part of the prior art base or were common general knowledge in the field relevant to the present disclosure as it existed before the priority date of each of the appended claims.